Creative gan generating art deviating from style norms

ABSTRACT

A method and system for generating art uses artificial intelligence to analyze existing art forms and then creates art that deviates from the learned styles. Known art created by humans is presented in digitized form along with a style designator to a computer for analysis, including recognition of artistic elements and association of particular styles. A graphics processor generates a draft graphic image for similar analysis by the computer. The computer ranks such draft image for correlation with artistic elements and known styles. The graphics processor modifies the draft image using an iterative process until the resulting image is recognizable as art but is distinctive in style.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present application claims the benefit of the earlier filing date ofU.S. provisional patent application No. 62/763,539, filed on Jun. 20,2018, entitled “Systems And Methods For Generating Art”, the contents ofwhich are hereby incorporated by reference as if fully contained herein.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to systems and methods for generating artand, more particularly, to such systems and methods which generate artthat deviates from learned styles of existing art.

Description of the Relevant Art

Since the dawn of artificial intelligence (AI), scientists have beenexploring the machine's ability to generate human-level creativeproducts such as poetry, stories, jokes, music, paintings, etc., as wellas creative problem solving. In terms of visual art, several systemshave been proposed to automatically create art, not only in the domainof AI and computational creativity but also in computer graphics, andmachine learning. Within the computational creativity literature,different algorithms have been proposed focused on investigating variousand effective ways of exploring the creative space.

In at least some cases in the past, an evolutionary process is usedwherein an artwork candidate is generated, evaluated using a fitnessfunction, and then modified to improve the fitness score for the nextiteration. The challenge for any such system is to create a logicalfitness function that has an “aesthetic sense”. Some earlier systemshave utilized a human in the loop with the role of guiding the process.For example, see Steve DiPaola and Liane Gabora, “Incorporatingcharacteristics of human creativity into an evolutionary art algorithm”,Genetic Programming and Evolvable Machines, 10(2):97-110, 2009. In theseinteractive systems, the computer explores the creative space, while thehuman plays the role of the observer whose feedback is essential indriving the process. Recent systems have emphasized the role ofperception and cognition in the creative process.

Deep neural networks have recently played a transformative role inadvancing artificial intelligence across various application domains. Inparticular, several generative deep networks have been proposed thathave the ability to generate images that emulate a given trainingdistribution. Generative Adversarial Networks (or “GANs”) have beensuccessful in achieving this goal. See generally “NIPS 2016 Tutorial:Generative Adversarial Networks”, by Ian Goodfellow, OpenAI, publishedApr. 3, 2017 (www.openai.com), the contents of which are herebyincorporated by reference as if fully set forth herein.

A GAN can be used to discover and learning regular patterns in a seriesof input data, or “training images”, and thereby create a model that canbe used to generate new samples that emulate the training images in theoriginal series of input data. A typical GAN has two sub networks, orsub-models, namely, a generator model used to generate new samples, anda discriminator model that tries to determine whether a particularsample is “real” (i.e., from the original series of training data) or“fake” (newly-generated). The generator tries to generate images similarto the images in the training set. The generator initially starts bygenerating random images, and thereafter receives a signal from thediscriminator advising whether the discriminator finds them to be “real”or “fake”. The two models, discriminator and generator, can be trainedtogether until the discriminator model is fooled about 50% of the time;in that case, the generator model is now generating samples of a typethat might naturally have been included in the original series of data.At equilibrium, the discriminator should not be able to tell thedifference between the images generated by the generator and the actualimages in the training set. Hence, the generator succeeds in generatingimages that come from the same distribution as the training set.

However, such GANs are limited in their ability to generate creativeproducts, since they are designed to simulate known styles/forms of art.Once again, the generator is trained to generate images that fool thediscriminator into believing that the generated image is from thetraining set; accordingly, if the training set consists of known worksof art, then the generator will simply generate images that look likealready existing art. There is no ability for known GANs to generateanything creative, i.e., there is no force that pushes the generator toexplore the creative space.

There are known extensions to GANs that facilitate generating imagesconditioned on categories or captions. A GAN can be designed to receivenot only training images but also labels that characterize the type ofstyle or art genre possessed by each such training image. For example,such labels might include, for example, “Renaissance”, “Impressionism”,“Baroque” or “Cubism”. However that does not lead to anything creative,either. It simply allows the GAN to characterize a generated work asbeing from among one of such styles. Today's human artists often striveto create new works that increase the arousal potential of their art, asby creating novel, surprising, ambiguous, and/or puzzling art. Thishighlights the fundamental limitation of using GANs in generatingcreative works.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide a newmethod and system for creating works of art using artificialintelligence, and producing artworks that include recognizable artisticelements while having novelty by comparison to known works of art.

It is another object of the present invention to provide such a methodand system that can be formed using modified Generative AdversarialNetworks, or GAN models.

It is still another object of the present invention to provide such amethod and system capable of creating works of art that deviate fromstyles of art already learned by such a GAN model.

Yet another object of the present invention is to provide such a methodand system capable of creating works of art that engender a significantdegree of surprisingness by comparison to works of art already learnedby such a GAN model.

Still another object of the present invention is to provide such amethod and system capable of creating works of art that engender asignificant degree of semantic ambiguity or puzzlingness by comparisonto works of art already learned by such a GAN model.

The present invention relates to the generation of creative art bymaximizing deviation from established styles, subjects and/or patternsof art, while minimizing deviation from works that are generallyrecognized as having artistic elements.

Briefly described, and in accordance with various embodiments, thepresent invention provides a method for generating art, and includingproviding to a computer a set of digitized data characterizing aplurality of existing works of art created by human beings; in at leastsome of the aforementioned embodiments, the digitized data also includesthe designation of a style classification associated with each suchexisting work of art. The computer is used to analyze the digitized dataand corresponding style designation data. The computer is trained torecognize artistic elements in such existing works of art, and createsart recognition data that associates particular artistic elements ofexisting works of art with particular style designations. This artrecognition data is stored in a memory of the computer. In at least someof such embodiments of the invention, the analysis of the digitized dataincludes the use of a neural network.

A graphics processing unit, or generator, generates a first iteration ofa graphic image which, in turn, is provided to the computer. Using thecomputer, the generated graphic image is compared to the art recognitiondata stored in the memory of the computer. Based upon such comparison,the computer provides an art index which ranks the generated graphicimage along a scale that ranges from being recognizable as art and notbeing recognizable as art. In at least some embodiments of theinvention, the computer also provides a novelty index which ranks thegenerated graphic art along a scale that ranges from being within aknown style for existing works of art and not being within a known stylefor existing works of art.

Based upon the art index and the novelty index, the first iteration ofthe generated graphic image is modified to have an art index that ismore recognizable as art and to have a novelty index that is less likeknown styles of existing works of art. These steps of comparing thegenerated graphic image to the stored art recognition data, providing anart index and novelty index, and modifying the generated image, arerepeated until the generated graphic image produces an art indexindicating that the generated graphic image is recognizable as art,while also producing a novelty index indicating the generated graphicimage is not within a known style for existing works of art.

In various embodiments of the invention, the method used to generate thegraphic image is an iterative enhancement process. In some embodimentsof the invention, the generated graphic image that is produced reachedafter performing such iterative enhancement process is rendered in atangible medium.

In various embodiments of the invention, the set of digitized dataanalyzed by the computer corresponds to digitized images of a number ofpaintings that are each fixed on a tangible medium. In other embodimentsof the aforementioned invention, the set of digitized data analyzed bythe computer corresponds to digitized images of a number of sculptureseach fixed within a tangible medium. In still other embodiments of theaforementioned invention, the set of digitized data analyzed by thecomputer corresponds to a number of image sequences. In yet otherembodiments of the present invention, the set of digitized data analyzedby the computer corresponds to a number of graphical designs. In furtherembodiments of the aforementioned invention, the set of digitized dataanalyzed by the computer corresponds to a number of fashion designs. Inyet further embodiments of the aforementioned invention, the set ofdigitized data analyzed by the computer corresponds to a number ofconsumer product designs.

Various embodiments of the present invention include a computerizedsystem for generating art, including a computer for receiving a set ofdigitized data characterizing a number of existing works of art createdby human beings. In at least some of the aforementioned embodiments, thedigitized data also includes the designation of a style classificationassociated with each such existing work of art. The computer analyzesthe digitized data and corresponding style designations, including therecognition of artistic elements within the existing works of art, andcreates art recognition data that associates particular artisticelements of existing works of art with particular style designations.This art recognition data may be stored in an electronic memoryassociated with the computer. In at least some of such embodiments ofthe invention, the analysis of the digitized data includes the use of aneural network.

A graphics processing unit is coupled to the computer for initiallygenerating a first iteration of a graphic image, and presenting suchgraphic image to the computer. The computer compares the generatedgraphic image to the stored art recognition data, and then provides anart index which ranks the generated graphic image along a scale thatranges from being recognizable as art and not being recognizable as art.In at least some embodiments of the invention, the computer alsoprovides a novelty index which ranks the generated graphic art along ascale that ranges from being within a known style for existing works ofart and not being within a known style for existing works of art.

The art index and novelty index are provided to the graphics processingunit, and in response, the graphics processing unit modifies the graphicimage further before presenting it to the computer. This process ofgenerating an image, analyzing such image with the computer, generatinga corresponding art index and novelty index, and providing the art indexand novelty index back to the graphics processing unit to further modifythe generated image, are repeated a sufficient number of times to createa final modified graphic image that results in an art index indicatingthat the generated graphic image is recognizable as art, while alsoproducing a novelty index indicating the generated graphic image is notwithin a known style or type for existing works of art. In someembodiments, a printer is included for printing the final modifiedgraphic image in a tangible medium.

In various embodiments of the computerized system, the set of digitizeddata analyzed by the computer corresponds to digitized images of anumber of paintings that are each fixed on a tangible medium. In otherembodiments of the computerized system, the set of digitized dataanalyzed by the computer corresponds to digitized images of a number ofsculptures each fixed within a tangible medium. In still otherembodiments of the computerized system, the set of digitized dataanalyzed by the computer corresponds to a number of image sequences. Inyet other embodiments of the computerized system, the set of digitizeddata analyzed by the computer corresponds to a number of graphicaldesigns. In further embodiments of the computerized system, the set ofdigitized data analyzed by the computer corresponds to a number offashion designs. In yet further embodiments of the computerized system,the set of digitized data analyzed by the computer corresponds to anumber of consumer product designs.

In various embodiments of the invention, a method for generating musicincludes providing to a computer a set of digitized data characterizinga number of existing musical compositions created by human beings, alongwith the designation of a style associated with each such existingmusical composition. The computer is used to analyze the digitized dataand corresponding style designations, including recognition of musicalelements in the existing musical compositions, and creating musicrecognition data associating particular existing musical compositionswith particular musical style designations. The music recognition datais stored in a memory associated with the computer.

A music synthesizer is provided to generate a first iteration of amusical composition. The generated musical composition is provided tothe computer, and the computer is used to compare the generated musicalcomposition to the stored music recognition data. Based upon suchcomparison, a music index is provided which ranks the generated musicalcomposition along a scale that ranges from being recognizable as musicand not being recognizable as music. Also, based upon such comparison, anovelty index is provided which ranks the generated musical compositionalong a scale that ranges from being within a known style for existingmusical compositions and not being within a known style for existingmusical compositions. The music index and novelty index are provided tothe music synthesizer to modify the generated musical composition. Thesteps of generating the musical composition, comparing it to storedmusic recognition data, providing the music index and novelty index tothe music synthesizer, and modifying the generated musical composition,are repeated until the generated musical composition produces a musicindex indicating that the generated musical composition is recognizableas music, while also producing a novelty index indicating the generatedmusical composition is not within a known style for existing musicalcompositions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for generating art deviating fromstyle norms in accordance with various embodiments of the presentinvention.

FIG. 2 is a graphical representation of a style ambiguity cross entropyloss function.

FIG. 3 is a block diagram of a system for generating art that maximizessurprisingness arousal in accordance with various embodiments of thepresent invention.

FIG. 4 is a block diagram of a system for generating art that maximizessemantic ambiguity in accordance with various embodiments of the presentinvention.

FIG. 5 is a block diagram of a system for generating musicalcompositions deviating from style norms in accordance with variousembodiments of the present invention.

DETAILED DESCRIPTION

The method disclosed herein is at least partially motivated from atheory suggested by Daniel. E. Berlyne (1924-1976), as set forth in“Arousal and reinforcement”, Nebraska symposium on motivation,University of Nebraska Press, 1967; and “Aesthetics and psychobiology”,Volume 336, JSTOR, 1971. Berlyne noted that the psychophysical conceptof “arousal” has a great relevance for studying aesthetic phenomena. The“level of arousal” measures how alert or excited a human being is. Thelevel of arousal varies from the lowest level, when a person is asleepor relaxed, to the highest level when s/he is violent, in a fury, or ina passionate situation. External stimulus patterns are one of themechanisms of arousal, and are of particular importance and relevance toart.

The term “arousal potential” refers to the properties of stimuluspatterns that lead to raising arousal. Besides other psychophysical andecological properties of stimulus patterns, Berlyne emphasized that themost significant arousal-raising properties for aesthetics are novelty,surprisingness, complexity, ambiguity, and puzzlingness. He coined theterm “collative variables” to refer to these properties collectively.

Novelty refers to the degree a stimulus differs from what an observerhas seen/experienced before. Surprisingness refers to the degree astimulus disagrees with expectation. Ambiguity refers to the conflictbetween the semantic and syntactic information in a stimulus.Puzzlingness refers to the ambiguity due to multiple, potentiallyinconsistent, meanings.

People prefer stimulus with a moderate arousal potential. Too littlearousal potential is considered boring, and too much stimulus activatesthe aversion system, which results in negative response. Of particularimportance in art is habituation, which refers to decreased arousal inresponse to repetitions of a stimulus.

Habituation is important in the field of art. If artists keep producingsimilar works of art, the arousal potential of such art is reduced, andthe desirability of that art is also reduced. Therefore, at any point oftime, artists will try to increase the arousal potential of art thatthey produce. In other words, habituation forms a constant pressure tochange art. However, this increase has to be tempered, as stimuli thatare slightly different, rather than vastly supernormal, are preferred.

The present invention provides a system and method for generating novelcreative works of art applicable to any domain of artistic forms andexpressions where the creative products can be digitized and manipulatedin digital format. This includes visual artistic forms such aspaintings, graphic designs, fashion designs, jewelry designs, sculpture,image sequence (moving picture), and other forms of art and designs. Inany of these forms, the analog content can be converted to a digitalformat (digitized) using a 2-dimensional scanner for planar objects likepaintings, or a 3-dimensional scanner for 3D objects like statues.Two-dimensional scanners include optical scanners and cameras.Three-dimensional scanners include stereo cameras, laser scanners, timeof flight cameras, structure light cameras, and methods that captureobject shapes using structure from motion technology or combiningmultiple views of the object using computer vision multi view geometry.

The system is also applicable to auditory forms such as music where thecontent can be digitized and encoded using frequency analysis(spectrograms) or using music transcription methods. This system is alsoapplicable to creative products that are originally created in digitalformat (born digital) such as computer graphic art, digital art,digitally generated music, etc. Therefore, as used herein, the words andphrases “art”, “works of art”, artwork” and “artworks” should beunderstood mean creative products in any of the aforementioned domainsincluding paintings, graphic designs, fashion designs, jewelry designs,sculptures, image sequences (moving picture), music, and other forms ofart and designs. It should be understood that the description hereinusing paintings as a form of artworks is only illustrative of thepresent disclosure. In any of the aforementioned domains, the input tothe system is a dataset extensively sampling prior human-generatedartworks in that domain comprising the historical prior work in thatdomain.

In practicing the present invention, various embodiments thereofprovides an art-generating system and method using a deep neural networkthat is a modified form of a Generative Adversarial Network, or GAN.GANs have become one of the most popular image synthesis models. A GANis typically trained by effectively “playing” a game between twoplayers. The first player, called the generator, G, generates samplesthat are intended to come from the same probability distribution as aset of training data, but without having access to such training data.The other player, denoted as the discriminator, D, examines the samplesto determine whether they are coming from the training data (i.e., theyare real) or not (they are fake). Both the discriminator and thegenerator are typically modeled as deep neural networks. The trainingprocedure is similar to a two-player min-max game with the followingobjective function:

$\begin{matrix}{{\min\limits_{G}\; {\max\limits_{D}\mspace{11mu} {V\left( {D,G} \right)}}} = {{E_{x\text{∼}{pdata}}\left\lbrack {\log \; {D(x)}} \right\rbrack} + {E_{z\text{∼}{pz}}\left\lbrack {\log \left( {1 - {D\left( {G(z)} \right)}} \right)} \right\rbrack}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

where z is a noise vector sampled from distribution p_(z) (e.g., uniformor Gaussian distribution) and x is a real image from the datadistribution p_(data). In practice, the discriminator and the generatorare alternatively optimized for every batch. The discriminator aims atmaximizing Equation 1 above by minimizing the expression below:

−E _(x-pdata)[log D(x)]−E _(z˜pz)[log(1−D(G(z)))],

which improves the utility of the D as a fake vs. real image detector.Meanwhile, the generator aims at minimizing Equation 1 above bymaximizing the term log(D(G(z)), which works better than −log(1−D(G(z)),since it provides stronger gradients. By optimizing D and Galternatively, GAN systems are trained to generate images that emulatethe training data distribution.

In contrast, various embodiments of the system and method disclosedherein generates art with increased levels of arousal potential in aconstrained way without activating the aversion system of a viewer. Inother words, the system generates artworks that are novel, but not toonovel. This criterion it is not easy to achieve, since it is not obviousto find a way to achieve that goal given the infinite possibilities inthe creative space. Various embodiments of the invention create andstore training information based upon existing works of art created byhumans. This training information can be continuously updated with theaddition of new art that becomes available from current artists. Theembodiments of the invention disclosed herein use this stored traininginformation in an indirect way while generating new art with arestrained increase in arousal potential. There are several ways toincrease such arousal potential, including novelty, surprisingness,complexity, ambiguity, and puzzlingness.

Various embodiments of the invention described herein focus uponincreasing the stylistic ambiguity and deviations from style norms,while at the same time, avoiding movement too far away from what iscommonly accepted as art. The system and method described herein allowsone to explore the creative space by deviating from the establishedstyle norms and thereby generate new works of art. Styles of art can beencoded using semantic style labels describing certain art movements,such as “impressionist” or “cubist” style. Artistic style can also bedefined using discrete or continuous time periods, such as 19th centurystyle. The rationale for considering novelty is that creative humanartists eventually break from established styles and explore new ways ofexpression to increase the arousal potential of their art. Creatorsoften work within a very structured domain, and it often takesconsiderable time for artists to break free from following establishedrules.

The method and system of the present invention, as included in variousembodiments described herein, are designed to generate art that does notfollow established art movements or styles, but instead generates artthat will confuse human viewers as to which style or form of art itbelongs. Arousal potential can also be quantified in terms of novelty.Surprisingness refers to the degree a stimulus disagrees withexpectation, which is quantified using the concept of informationcontent from information theory. Unlike novelty and surprisingness whichrely on inter-stimulus comparisons of similarity and differences,complexity is an intra-stimulus property that increases as the number ofindependent elements in a stimulus grows. Complexity is quantified usingintra-stimulus entropy and/or self-correlation measures. Semanticambiguity refers to the conflict between the semantic and syntacticinformation in a stimulus. Puzzlingness refers to the ambiguity due tomultiple, potentially inconsistent, meanings. Semantic ambiguity andpuzzlingness are quantified by measuring the entropy of the output ofobject classifier or genre classifier.

In the block diagram of FIG. 1, a discriminator 100 is formed as acomputer model on a general purpose computer, which includes memory 102.Discriminator 100 includes two input ports 104 and 106. Input port 104is coupled to human art sample block 108, which may represent a cameraor scanner for supplying digitized data to discriminator 100. In turn,block 108 is coupled to art source blocks 110 which represent existingworks of art created in the past by humans. Each such work of artincluded in source blocks 110 may include a label associated therewith.These labels may, for example, designate a particular art styleassociated with each such work. Alternatively, such labels maycharacterize the content of each such work, including scenes, objects,or people depicted in each such work.

In order to “train” the system, all of the works of art, and each oftheir associated labels, included in source blocks 110, are passedthrough block 108 for presentation as digitized data to discriminator100. Discriminator 100 has two output nodes 112 and 114. Output node 112is coupled to block 116, labeled “Art/Not Art”, which provides an artindex value. The art index value varies along a range from, e.g., a highvalue indicating that a work has a high degree of recognized artisticelements, to a low value indicating that a work has a significantabsence of recognized artistic elements. This art index value is fedback to discriminator 100 by data bus 118. During “training”, art indexvalues are purposely configured to a high value, since discriminator 100is analyzing an existing known work of actual art.

Node 114 is coupled to Art Style Classification block 120 for receivingstyle label information associated with the current art sample beinganalyzed. The art style information might correspond, for example, toExpressionism, Abstract-Expressionism, Action-Painting,Color-Field-Painting, Minimalism, Cubism, Analytical-Cubism,Synthetic-Cubism, Naive Art-Primitivism, Art-Nouveau-Modern, Realism,Contemporary-Realism, New-Realism, Baroque, Early-Renaissance,High-Renaissance, Northern-Renaissance, Pointillism, Pop-Art,Impressionism, Post-Impressionism, Rococo, Fauvism, or Romanticism, toname a few. The output from block 120 is also sent along bus 118 back todiscriminator 100.

Art style classification box 120 also provides its output to styleambiguity box 130. During “training”, there is no “ambiguity”, sinceeach work of art analyzed by discriminator 100 includes a pre-assignedlabel characterizing the work as being of one type of style or another.Style ambiguity box 130 provides an output value that is relatively low(zero) when the style of the work is clearly recognized, and provides anoutput value that is relatively high when the style of the work beinganalyzed does not correspond well to one of the style types providedduring “training”. The output of style ambiguity block may be viewed asone example of a novelty index for rating the novelty of a work underanalysis. Again, during “training”, none of the works are regarded asbeing novel.

When “training” has been completed, i.e., when all of the works of artin source blocks 110 have been input to discriminator 100, memory 102has accumulated a wealth of data regarding artistic elements that arecommonly present in existing works, as well as the particular styles ofworks that typically include those respective artistic elements.

Still referring to FIG. 1, generator 140, which may be a graphicsprocessing unit, is provided to generate an image that may ultimatelyserve as a work of art. The output of generator 140 is provided to input106 of discriminator 100. Generator 140 is also coupled to a data bus142 for receiving feedback information from blocks 116 and 130.Generator 140 is also coupled to input vector block 146 for receivingrandomized input vectors that initially drive the image created bygenerator 140.

Based upon input vectors provided by block 146, generator 140 initiallyproduces a randomized image at its output 106 to discriminator 100.Discriminator 100 then analyzes the image presented by generator 140 inat least two ways. First, discriminator 100 looks for patternsresembling artistic elements that discriminator 100 found in existingworks of art during training. Secondly, discriminator 100 compares anysuch artistic elements that are present in the generated work toartistic elements known to be associated with particular styles of worksthat were analyzed during training.

During a first iteration, and perhaps during the first fifty iterationsof images generated by generator 140, it is likely that block 116 willproduce a very low “art index”, indicating that few, if any, artisticelements were found to be present in the generated work. The output ofblock 116, the “art index”, is provided over data bus 142 to generator140, and in response, generator 140 modifies the most-recently generatedimage to “try again”. Slowly, but eventually, the art index provided byblock 116 will increase, by finding a greater number of artisticelements and patterns in the generated work. If desired, this iterativeprocess can be stopped before the art index becomes too high in value,particularly if the objective is to generate works that are novel. Onone hand, the generated work should include enough recognizable artisticelements that a viewer will consider the work to be “artistic”. While itis desired to generate novel artwork, the resulting work of art shouldnot be “too novel”, or it will generate too much arousal, therebyactivating the viewer's aversion system. 3) the generated work shouldincrease the stylistic ambiguity.

The other signal fed back to generator 140 is the novelty index, orstyle ambiguity rating generated by block 130. One way of ensuring thatthe generated work of art is novel is to create a work that does notclearly match known art styles on which the system has already beentrained. In the first iteration of the image generated by generator 140,the novelty index will almost certainly be relatively high since theinitially generated image will not have any artistic elements thatresemble any known art style. However, after many iterations, it couldbe the case that generator 140 is producing an image that not only hasrecognized artistic elements, but also has a number of particularartistic elements that are typically found in a particular style of art.If the objective is to create novel art, then generator 140 needs to besteered away from producing a work that fits closely within a knownstyle of art encountered during “training”. Thus, by feeding the outputof block 130 onto data bus back to generator 140, generator 140 can makemodifications to retain enough novelty to have an ambiguous art style,i.e., to have a style that is not easily classified within one or moreknown art styles.

The system shown in FIG. 1 includes two “adversary networks”,discriminator 100 and generator 140. Discriminator 100 has access to alarge set of art (110) associated with style labels (Renaissance,Baroque, Impressionism, Expressionism, etc.) and uses it to learn todiscriminate between styles. On the other hand, generator 140 does nothave access to any pre-existing art; it generates art starting from arandom input. Unlike a conventional Generative Adversarial Network, orGAN, generator 140 receives two signals from discriminator 100 for eachwork it generates. The first signal is the discriminator'sclassification of “art or not art”, i.e., the “art index” supplied byblock 116. In a traditional GAN, this signal enables the generator tochange its weights to generate images that are more likely to fooldiscriminator 100 as to whether such generated image is an actual workof art created by a human. In a conventional GAN, this is the onlysignal received by the generator from the discriminator, whichessentially compels the generator to eventually converge to generateimages that will emulate known works of art.

In contrast to a conventional GAN, generator 140 receives a secondsignal, provided by block 130, indicating whether, and to what extent,discriminator 100 can classify the generated image into one or moreestablished styles. If generator 140 generates an image thatdiscriminator 100 thinks to be art, and if discriminator 100 can alsoeasily classify the generated image into one of the art styles that wereestablished during training, then generator 140 would have fooleddiscriminator 100 into considering the generated image as being actualexisting art that fits within an established art style. In contrast, thesystem of FIG. 1 is configured to “fool” the discriminator to considerthe generated image to be “art,” but causes discriminator 100 toconclude that the style of the generated work is ambiguous.

The art index signal provided by block 116 and the style ambiguity (ornovelty) signal provided by block 130 are contradictory forces, becausethe art index signal pushes generator 140 to generate works thatdiscriminator 100 accepts as “art,” but the style ambiguity signal willpushes generator 140 to generate style-ambiguous works. Therefore, thesetwo signals, working together, cause generator 140 to better exploreparts of the creative space that lie close to recognizable art whileincreasing the ambiguity of the generated art with respect to how itfits in the realm of standard art styles.

Still referring to FIG. 1, it has been mentioned above that block 130provides a signal representing style ambiguity. Style ambiguity can beincreased by maximizing the style class posterior entropy. Inmathematical terms, this is equivalent to making generator 140 producean image x˜p_(data) while maximizing the entropy of p(c|x) (i.e., thestyle class posterior) for the generated images. However, instead ofmaximizing the class posterior entropy, one may instead minimize thecross entropy between the class posterior and a uniform targetdistribution. Similar to entropy that is maximized when the classposteriors (i.e., p(c|G(z))) are equiprobable, cross entropy withuniform target distribution will be minimized when the classes areequiprobable. So both objectives will be optimal when the classes areequiprobable. FIG. 2 is a graph that shows the style ambiguity crossentropy loss function. In FIG. 2, the upper curve shows entropy, whilethe lower curve shows cross entropy with a uniform distribution(inverted for purposes of illustration). As shown in FIG. 2, the crossentropy curves sharply toward negative infinity at the boundary if anyclass posterior approaches 1 (or zero), while entropy goes to zero atthis boundary condition. Therefore, one may use the cross entropy tosignal that the generated image can be easily classified into one of theart style classes which discriminator 100 learned during training. Thisin turn would generate a very large loss, and hence, large gradients, ifthe generated image closely resembles one of the trained art styleclasses.

Hence, we can redefine the loss function of the system, now with adifferent adversarial objective, as follows:

$\begin{matrix}{{\min\limits_{G}{\underset{D}{\; \max}\mspace{11mu} {V\left( {D,G} \right)}}} = {{_{x,{\hat{c} \sim p_{data}}}\left\lbrack {{\log \; {D_{r}(x)}} + {\log \; {D_{c}\left( {c = {\hat{c}x}} \right)}}} \right\rbrack} + {E_{z\text{∼}{pz}}\left\lbrack {{\log \left( {1 - {D_{r}\left( {G(z)} \right)}} \right)} - {\sum\limits_{k = 1}^{K}\; \left( {\frac{1}{K}{\log \left( {{D_{c}\left( {c_{k}{G(z)}} \right)} + \left( {1 - {\frac{1}{K}{\log \left( {1 - {D_{c}\left( {c_{k}{G(z)}} \right)}} \right)}}} \right\rbrack} \right.}} \right.}} \right.}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

where z is a noise vector sampled from distribution p_(z) (e.g., uniformor Gaussian distribution) and x and c{circumflex over ( )} are a realimage and its corresponding style label from the data distribution (ortraining data) p_(data). D_(r)(⋅) is the transformation function thattries to discriminate between real art and generated images. D_(c)(⋅) isthe function that discriminates between different style categories andestimates the style class posteriors (i.e., D_(c)(ck|⋅)=p(c_(k)|⋅)).

In reference to Equation 2 above, discriminator 100 encouragesmaximizing Equation 2 by minimizing the term −E_(x-pdata)[logD_(r)(x)+log D_(c)(c=c{circumflex over ( )}|x)] for the real images ofactual existing artwork, and minimizing the expression−E_(z˜pz)[log(1−D_(r)(G(z)))] for the generated images. Discriminator100 is trained not only to discriminate the real art samples from thegenerated (fake) ones, but also to identify their style class though theK-way loss (where K is the number of style classes). Therefore,discriminator 100 is simultaneously learning about both the artdistribution (artistic elements) and art styles (style classifications).

Generator 140 encourages minimizing Equation 2 by maximizing theexpression

${D_{r}\left( {G(z)} \right)} - {\sum\limits_{k = 1}^{K}\left( {\frac{1}{K}{\log \left( {{D_{c}\left( {c_{k}{G(z)}} \right)} + {\left( {1 - \frac{1}{K}} \right){\log\left( {1 - {{D_{c}\left( {c_{k}{G(z)}} \right)}.}} \right.}}} \right.}} \right.}$

In the expression above, the first term, D_(r)(G(z)), corresponds theresemblance of the generated image to real art, and the second term(i.e., the summation from k=1 through K) corresponds to degree of styleambiguity. This pushes the generated images to look more like real art(first term), but also to have a large cross entropy for p(c|G(z)) witha uniform distribution to maximize style ambiguity (second term).

One example of a training process that may be used to train generator140 can be represented by the computer instruction steps listed below(with step size a, using mini-batch SGD for simplicity):

 1: Input: mini-batch images x, matching label ĉ, number of trainingbatch steps S  2: for n = 1 to S do  3:  z ~ N(0,1)^(Z) {Draw sample ofrandom noise}  4:  {circumflex over (x)} ← G(z) {Forward throughgenerator}  5:  s_(D) ^(r) ← D_(r)(x) {real image, real/fake loss}  6: s_(D) ^(c) ← D_(c)(ĉ|x) {real image, multi class loss}  7:  s_(G) ^(f)← D_(r)({circumflex over (x)}) {fake image, real/fake loss}  8:  $\left. s_{\alpha}^{c}\leftarrow{\sum_{k = 1}^{K}{\frac{1}{K}{\log\left( {{p\left( c_{k} \middle| \hat{x} \right)} + {\left( {1 - \frac{1}{K}} \right)\left( {\log \left( {p\left( c_{k} \middle| \hat{x} \right)} \right)} \right.}} \right.}}} \right.$ 9:  L_(D) ← log(S_(D) ^(r)) + log(1 − s_(G) ^(f)) 10:  D ← D − α∂LD/∂D{Update discriminator} 11:  L_(G) ← log(s_(G) ^(f)) − s_(G) ^(c) 12:  G← G − α∂LG/∂G {Update generator} 13: end forFor generator 140, the first z E R100 normally sampled from 0 to 1 isup-sampled to a 4× spatial extent convolutional representation with 2048feature maps resulting in a 4×4×2048 tensor. Then a series of fourfractionally-stride convolutions are derived. Finally, this high levelrepresentation is converted into a 256×256 pixel image. In other words,starting from z E R100, a 4×4×1024 image is created, then converted toan 8×8×1024 image, then converted to a 16×16×512 image, then convertedto a 32×32×256 image, then to a 64×64×128 image, then to a 128×128×64image, and finally to a 256×256×3 generated image size.

In the example described above, discriminator 100 has two types oflosses, namely, real/fake loss and multi-label loss. Discriminator 100starts with a common body of convolution layers followed by two heads(one for the real/fake loss and one for the multi-label loss). Thecommon body of convolution layers is composed of a series of sixconvolution layers (all with stride 2 and 1 pixel padding), as follows:

conv1 (32 4×4 filters)

conv2 (64 4×4 filters)

conv3 (128 4×4 filters)

conv4 (256 4×4 filters)

conv5 (512 4×4 filters)

conv6 (512 4×4 filters).

Each of the above-listed convolutional layers may be followed by a leakyrectified activation (LeakyRelU) in all the layers of the discriminator.Such leaky-rectified activation layers are described, for example, inMaas, et al., “Rectifier nonlinearities improve neural network acousticmodels”, In Proc. ICML, volume 30, 2013; and Xu, et al., “Empiricalevaluation of rectified activations in convolutional network”, arXivpreprint arXiv: 1505.00853, 2015. After passing a image to the commoncony D body, it will produce a feature map of size 4×4×512. Thereal/fake D_(r) head collapses the 4×4×512 feature map to produceD_(r)(c|x) (probability of image being sourced from real artwork). Themulti-label probabilities D_(c)(c_(k)|x) head is produced by passing the4×4×512 feature map into 3 fully collected layers sizes 1024, 512, K,respectively, where K is the number of style classes.

Referring again to the example training process set forth above fortraining generator 140, as exemplified by the computer instructionslisted earlier, the weights were initialized from a zero-centered Normaldistribution with standard deviation 0.02. A mini-batch size of 128 wasused. For training purposes, a mini-batch stochastic gradient descent(SGD) was used, with a learning rate of 0.0001. In the LeakyReLU, theslope of the leak was set to 0.2 in all models. Others who have employeda conventional GAN have used momentum to accelerate training. Theapplicant has found that use of an “Adam optimizer” can be advantageous,particularly if the model is trained for 100 epochs (i.e., 100 passesover the training data). To stabilize the training, the BatchNormalization process described in Ioffe, et al., “Batch normalization:Accelerating deep network training by reducing internal covariateshift”, arXiv preprint arXiv: 1502.03167, 2015 may be used to normalizethe input to each unit to have zero mean and unit variance. Dataaugmentation may be performed by adding five crops for each image(bottom-left, bottom-right, mid, top-left, top-right) within the imagedataset. The width and height of each crop is 90% of the width and theheight of the original painting.

Those skilled in the art will appreciate that the network trainingprocess described above is only one example that may be successfullyemployed. Other GAN training protocols and modeling methods alreadyknown to those skilled in the art for implementing a generativeadversarial network, including its discriminator and generator, may alsobe used, if desired, to achieve the objectives of the present invention.

Turning to FIG. 3, the model described above in reference to FIG. 1 canbe modified to realize a variant model that maximizes the“surprisingness” of the generated art as a force which opposes the“art/not art” factor in the loss function. Elements within FIG. 3 thatcorrespond with like elements in FIG. 1 have been labeled with similarreference numerals. The primary difference between the system of FIG. 3compared with the system of FIG. 1 is that a) the labels associated withthe art images in source files 210 provide information regardinginformation content in the existing works of art, rather thancharacterizing the art style of such works, and b) discriminator 200provides information to block 250 regarding perceived content of animage rather than providing information regarding the style of the work.

The output signal of Information Content block 250 provides a rankingranging from a low value (indicating content is recognized) and a highvalue (indicating that content is highly ambiguous, or novel. Both theart index generated by block 216 and the novelty index generated byblock 250 are provided by data bus 242 to generator 240. The output ofblock 250 pushes generator 240 to generate samples that are on theboundary of the training samples instead of emulating the trained art.This is achieved by measuring the information content in the generatedsamples, and generating a surprisingness value, using Shannoninformation theory measure, as:

surprisingness(x)=I(x|M)=−log(x|M)

where M is the discriminator memory, or a representative sample thereofapproximating the content distribution of the trained samples.

FIG. 4 shows another variant of the block diagram of FIG. 1, this timefor maximizing the semantic ambiguity and/or puzzlingness of thegenerated art as the force opposing the “art/not art” index in the lossfunction. Elements within FIG. 3 that correspond with like elements inFIG. 1 have been labeled with similar reference numerals. The primarydifference between the system of FIG. 4 compared with the system of FIG.1 is that a) the labels associated with the art images in source files310 provide information regarding semantic information relative to eachof the existing works of art, rather than characterizing the art styleof such works, and b) discriminator 200 provides information to block360 regarding perceived semantic classification

of an image rather than providing information regarding the style of thework.

The system of FIG. 4 pushes generator 340 to explore parts of the spacethat create semantically surprising art. This is realized using semanticclassifier 360 at discriminator 300 where it learns to classify artaccording to specific semantic labels. These semantic labels can be aset of object classes expected to be seen in art images, or a set ofsubject matters, or a set of genres. A semantic ambiguity term, providedby block 330, can be realized using cross-entropy among the semanticclasses similar to the case of stylistic ambiguity.

An embodiment of the present invention was tested to validate theconcepts described herein. For purposes of training the system,paintings from the publicly available “WikiArt” dataset found atwww.wikiart.org. This collection (as downloaded in 2015) included imagesof 81,449 paintings from 1,119 artists ranging from the Fifteenthcentury to Twentieth century. The listing below shows the number ofimages from each style used in training the model:

Style name Number of Images Abstract-Expressionism 2,782 Action-Painting98 Analytical-Cubism 110 Art-Nouveau-Modern 4,334 Baroque 4,241Color-Field-Painting 1,615 Contemporary-Realism 481 Cubism 2,236Early-Renaissance 1,391 Expressionism 6,736 Fauvism 934 High-Renaissance1,343 Impressionism 13,060 Mannerism-Late-Renaissance 1,279 Minimalism1,337 Naive Art-Primitivism 2,405 New-Realism 314 Northern-Renaissance2,552 Pointillism 513 Pop-Art 1,483 Post-Impressionism 6,452 Realism10,733 Rococo 2,089 Romanticism 7,019 Synthetic-Cubism 216 Total 75,753

Images were then generated according to three different baseline models,and assessed for artistic merit by human evaluators. In the firstbaseline model, the images were generated with 64×64 resolution.However, it was found that this model failed to generate images thatemulate the trained art, i.e., the generated samples did not show anyrecognizable figures or art genres or styles.

Using the second baseline model, two more “layers” were added to thegenerator to increase the resolution of the generated images to 256×256.In this case, the generated samples showed significant improvement;human evaluators could clearly see aesthetically appealing compositionalstructures and color contrasts in the resulting images. However, thegenerated images did not show any recognizable figures, subject mattersor art genres.

The third baseline model included style classification loss function,but not the style ambiguity loss function. In this model, thediscriminator learns to discriminate between style classes as it istrained on the images associated with those art styles. The generatorapplied exactly the same loss as the discriminator model; in otherwords, the generator was merely trying to deceive the discriminator byemulating works of art already encountered during training. In the caseof the third baseline model, unlike the two first baselines that merelyrespond to the “art/not art” index, this model also learn about stylesclasses in art. This third baseline model can be referred to as“style-classification-CAN”, where CAN designates “Creative AdversarialNetworks”, as contrasted with GAN (“Generative Adversarial Networks”).The generated images of this model showed significant improvement inactually emulating the trained art distribution; human evaluators couldidentify “hallucinations” of portraits, landscapes, architectures,religious subject matter, etc. This third baseline model demonstratedthat the style-classification-CAN model (without using the styleambiguity loss function) could better emulate the trained artdistribution by learning about style classes. However, the generatedimages did not have a high degree of creativity.

After running the first three baseline models, as described above, anembodiment of the present invention as represented and described inregard to FIG. 1 was used, including the style ambiguity loss function.The generated images were then ranked for artistic merit by humanevaluators. In contrast to the aforementioned three baseline models, theembodiment represented in FIG. 1 generated images that could becharacterized as novel and not emulating the trained art distribution,and yet still aesthetically appealing. The generated images of the CANmodel (using the style ambiguity loss function) do not show typicalfigures, genres, styles, or subject matter, since the style ambiguityloss forces the network to try to generate novel images while retainingartistic elements learned from the training art distribution, andthereby being aesthetically appealing.

Human subject experiments were conducted to evaluate aspects of thecreativity of the CAN model. The goal of such experiments was to testwhether human subjects could tell whether the CAN-generated images weregenerated by a human artist or by a computer system, and to determinewhether the CAN-generated images were regarded as having artistic merit.All generated images were upscaled to 512×512 resolution using asuperresolution algorithm. In many instances, human evaluators foundthat works generated by the embodiment shown in FIG. 1 were more likelyto have been produced by a human artist than some of the actual existingworks used to train the model, and in several cases, the humanevaluators liked the computer generated images produced by theembodiment shown in FIG. 1 more than similar images created by realartists. In this regard, human evaluators found the images generated bythe system of FIG. 1 to be intentional, visually structured,communicative, and inspiring, with similar levels to actual human art.These results indicate that the human evaluators see thesecomputer-generated images as art!

A separate experiment was conducted to evaluate the effect of adding thestyle ambiguity loss to the CAN model, in contrast to the styleclassification loss, for purposes of generating novel and aestheticallyappealing images. One of the objectives of the experiment was to try todetermine whether the basis for creativity (novelty) in the CAN modelcomes from the mere learning about art styles, or whether suchcreativity comes from intentional deviation from known art styles? Toassess creativity, the novelty of the generated images was explored witha pool of art history students; such sophisticated art-educated studentsare well-adapted to judge the novelty and aesthetics of computergenerated images. Each subject was shown pairs of images, one generatedby CAN including use of the style ambiguity loss function, and onegenerated by CAN using the style classification loss function, butomitting the style ambiguity loss function. The respective paired imageswere randomly selected and placed in random order side by side. Humanevaluators were asked, regarding the paired images, which image wasregarded as being more novel, and which image was regarded as being moreaesthetically appealing.

The results of this experiment showed that 59.47% of the time, the humanevaluators selected the CAN images (generated using the style ambiguityindicator loss function) as being more novel. Also, the results showedthat 60% of the time, the human evaluators selected the CAN images(generated using the style ambiguity indicator loss function) as beingmore aesthetically appealing. These results are believed to indicatethat use of the style ambiguity loss function in the process ofgenerating images using CAN serves to increase novelty of the generatedworks without sacrificing artistic appeal.

FIG. 5 is a block diagram of a similar CAN system but directed to thecreation of novel musical compositions and/or audible works, rather thanvisual works. However, the general operation of the system shown in FIG.5 is virtually the same as for the system of FIG. 1, and like componentsare identified by similar reference numerals. A source of existingmusical works 410 are individually provided to block 408 for input todiscriminator 400 for training purposes, including style labelsassociated with each such work. Discriminator 400 “learns” that each ofthe source works is actual music, and the style of music in which suchwork is classified. Following training, generator 440, which may be aform of music synthesizer or audio generator, is initially driven byrandom input vector block 446 to generate a series of random sounds,notes, and/or chords for presentation to discriminator 400. Block 416ranks the generated composition on the music/non-music scale, and styleambiguity block 430 ranks the generated composition for music styleambiguity. These “music/non-music” and “music style ambiguity” rankingsare provided on data bus 442 to generator 440 for modifying the nextiteration of the generated musical composition. After a number ofiterations, the generated musical composition will include recognizablemusical elements, but will have a relatively high degree of noveltycompared to existing musical compositions produced by human composers.It should be understood that remarks made herein with regard to works ofart may include musical compositions as well.

Those skilled in the art should now appreciate that a method and systemfor generating art with creative characteristics has been disclosedbased upon a novel creative adversarial network, or CAN. The system isfirst trained using a large collection of art images with style labels(or, alternatively, with other labels characterizing the type ofartwork). The system is then able to generate art by optimizing acriterion that stays close to commonly-recognized artistic elements,while increasing stylistic ambiguity. Images generated by usingApplicant's method and system were sometimes mistaken by human subjectsas having been generated by human artists, and were sometimes found tobe relatively more appealing than works created by human artists. Allmachine learning applied herein is based solely on exposure to art andconcepts of art styles. The system has the ability to continuously learnfrom new training art and would then be able to adapt its generationbased on what it learns.

Several embodiments are specifically illustrated and/or describedherein. However, it will be appreciated that modifications andvariations are covered by the above teachings and within the scope ofthe appended claims without departing from the spirit and intended scopethereof. It should be understood that the description, and specificembodiments, discussed herein are merely illustrative of the presentinvention. Various modifications or adaptations of the methods describedmay become apparent to those skilled in the art and/or devised by thoseskilled in the art without departing from the disclosure. All suchmodifications, adaptations, or variations that rely upon the teachingsof the present invention, and through which these teachings haveadvanced the art, are considered to be within the spirit and scope ofthe present invention. Hence, these descriptions and drawings should notbe considered in a limiting sense, as it is understood that the presentinvention is in no way limited to only the embodiments illustrated. Thepresent disclosure is intended to embrace all such alternatives,modifications and variances. The embodiments described are presentedonly to demonstrate certain examples of the disclosure. Other elements,steps, methods, and techniques that are insubstantially different fromthose described above and/or in the appended claims are also intended tobe within the scope of the disclosure.

I claim:
 1. A method for generating art comprising the steps of: a)providing to a computer a set of digitized data characterizing aplurality of existing works of art created by human beings, thedigitized data including the designation of a style associated with eachsuch existing work of art, the computer including a memory; b) using thecomputer to analyze the digitized data and corresponding styledesignations, including recognition of artistic elements in the existingworks of art, and creating art recognition data associating particularartistic elements of existing works of art with particular styledesignations; c) storing the art recognition data in the memory of thecomputer d) providing a graphics processing unit to generate a graphicimage; e) providing the generated graphic image generated by thegraphics processing unit to the computer; f) using the computer tocompare the generated graphic image to the stored art recognition data;g) based upon such comparison, providing an art index which ranks thegenerated graphic image along a scale that ranges from beingrecognizable as art and not being recognizable as art; h) based uponsuch comparison, providing a novelty index which ranks the generatedgraphic art along a scale that ranges from being within a known stylefor existing works of art and not being within a known style forexisting works of art; i) modifying the generated graphic image basedupon the art index and the novelty index; j) repeating steps f) throughi) until the generated graphic image produces an art index indicatingthat the generated graphic image is recognizable as art, while alsoproducing a novelty index indicating the generated graphic image is notwithin a known style for existing works of art.
 2. The method recited byclaim 1 wherein such method is an iterative enhancement process.
 3. Themethod recited by claim 1 wherein the generated graphic image reachedafter performing such iterative enhancement process is rendered in atangible medium.
 4. The method recited by claim 1 wherein the set ofdigitized data comprises a plurality of paintings fixed on tangiblemedium.
 5. The method recited by claim 1 wherein the set of digitizeddata comprises a plurality of sculptures fixed on tangible medium. 6.The method recited by claim 1 wherein the set of digitized datacomprises a plurality of image sequences.
 7. The method recited by claim1 wherein the set of digitized data comprises a plurality of graphicaldesigns.
 8. The method recited by claim 1 wherein the set of digitizeddata comprises a plurality of fashion designs.
 9. The method recited byclaim 1 wherein the set of digitized data comprises a plurality ofconsumer product designs.
 10. The method recited by claim wherein thestep of using the computer to analyze the digitized data includes theuse of a neural network.
 11. A method for generating music comprisingthe steps of: a) providing to a computer a set of digitized datacharacterizing a plurality of existing musical compositions created byhuman beings, the digitized data including the designation of a styleassociated with each such existing musical composition, the computerincluding a memory; b) using the computer to analyze the digitized dataand corresponding style designations, including recognition of artisticelements in the existing musical compositions, and creating musicrecognition data associating particular existing musical compositionswith particular style designations; c) storing the music recognitiondata in the memory of the computer d) providing a music synthesizer togenerate a musical composition; e) providing the generated musicalcomposition generated by the music synthesizer to the computer; f) usingthe computer to compare the generated musical composition to the storedmusic recognition data; g) based upon such comparison, providing a musicindex which ranks the generated musical composition along a scale thatranges from being recognizable as music and not being recognizable asmusic; h) based upon such comparison, providing a novelty index whichranks the generated graphic musical composition along a scale thatranges from being within a known style for existing musical compositionsand not being within a known style for existing musical compositions; i)modifying the generated musical composition based upon the music indexand the novelty index; j) repeating steps f) through i) until thegenerated musical composition produces a music index indicating that thegenerated musical composition is recognizable as music, while alsoproducing a novelty index indicating the generated musical compositionis not within a known style for existing musical compositions.
 12. Acomputerized system for generating art comprising in combination: a) acomputer for receiving a set of digitized data characterizing aplurality of existing works of art created by human beings, thedigitized data including the designation of a style associated with eachsuch existing work of art; b) an electronic memory coupled to thecomputer; c) the computer analyzing the digitized data and correspondingstyle designations, including recognition of artistic elements in theexisting works of art, and creating art recognition data associatingparticular artistic elements of existing works of art with particularstyle designations for storage in the electronic memory; d) a graphicsprocessing unit coupled to the computer for generating a graphic image,and presenting such graphic image to the computer; f) the computercomparing the generated graphic image to the stored art recognitiondata, and the computer providing an art index which ranks the generatedgraphic image along a scale that ranges from being recognizable as artand not being recognizable as art, the computer also providing a noveltyindex which ranks the generated graphic art along a scale that rangesfrom being within a known style for existing works of art and not beingwithin a known style for existing works of art; g) the graphicsprocessing unit receiving information associated with the art index andthe novelty index, and being responsive thereto to generate a modifiedgraphic image for presentation to the computer; whereby the graphicsprocessing unit repeatedly presents modified graphic images to thecomputer until a final modified graphic image presented thereby to thecomputer produces an art index indicating that the generated graphicimage is recognizable as art, while also producing a novelty indexindicating the generated graphic image is not within a known style forexisting works of art.
 13. The computerized system recited by claim 12further including a printer for printing the final modified graphicimage in a tangible medium.
 14. The computerized system recited by claim12 wherein the set of digitized data comprises a plurality of paintingsfixed on tangible medium.
 15. The computerized system recited by claim12 wherein the set of digitized data comprises a plurality of sculpturesfixed on tangible medium.
 16. The computerized system recited by claim12 wherein the set of digitized data comprises a plurality of imagesequences.
 17. The computerized system recited by claim 12 wherein theset of digitized data comprises a plurality of graphical designs. 18.The computerized system recited by claim 12 wherein the set of digitizeddata comprises a plurality of fashion designs.
 19. The computerizedsystem recited by claim 12 wherein the set of digitized data comprises aplurality of consumer product designs.
 20. The computerized systemrecited by claim 12 further including a neural network for recognizingartistic features in works of art.